A GAPN Approach for the Flexible Job-Shop Scheduling Problem with Indirect Energy and Time-of-Use Electricity Pricing

The flexible job-shop scheduling problem with indirect energy and time-of-use (ToU) electricity pricing (FJSP-IT) is investigated. Considering the production cost, which includes direct cost time evaluation model under ToU built. To minimize total of FJSP-IT, an approach based on a genetic algorithm Petri nets (GAPN) presented. Under this approach, are modeled net (PN) nodes, operation evaluated through PN simulation, resource allocation fine-tuned operations. A group heuristic policies, especially exhausting subsection policy two mixed presented to adapt FJSP-IT vague components. Experiments were performed data set generated from banburying shop rubber tire plant, results show that proposed GAPN has good convergence. Using policies makes it possible save 10.81% compared using single off-peak first or passive delay policy, considering at least 2.09% ignoring energy.